Reinforcement Learning
A Long N-step Surrogate Stage Reward for Deep Reinforcement Learning Junmin Zhong Arizona State University Ruofan Wu Arizona State University Jennie Si Arizona State University
DDPG (TD3) [7], have demonstrated their great potential. Contributions . 1) We introduce a new, simple yet effective surrogate reward They usually proceed as follows.
Constraint-Conditioned Policy Optimization for Versatile Safe Reinforcement Learning
CCPO can generalize to diverse unseen constraint thresholds without retraining the policy.
A Partially Supervised Reinforcement Learning Framework for Visual Active 440 Search: Supplementary Material 441 A Policy Network Architecture and Hyperparameter Details
Table 13: ANT comparisons when trained with small car as target on xView in multi-query setting.
A Partially Supervised Reinforcement Learning Framework for Visual Active Search
Moreover, query results (e.g., detected search and rescue activity in a particular region) are highly informative about the locations of target objects in other regions, for example, due to spatial
Deep Recurrent Optimal Stopping
Deep neural networks (DNNs) have recently emerged as a powerful paradigm for solving Markovian optimal stopping problems. However, a ready extension of DNN-based methods to non-Markovian settings requires significant state and parameter space expansion, manifesting the curse of dimensionality.
Truncating Trajectories in Monte Carlo Policy Evaluation: an Adaptive Approach
However, is this data collection strategy the best option?